Increasingly, modern data centers are designed with a heterogeneous mixture of computing nodes including “fat” computing nodes, “thin” computing nodes, and dedicated nodes to accelerate important functions. Fat computing nodes are nodes with multiple sockets of high-end symmetrical multiprocessors (SMPs) with large memory spaces, while thin computing nodes are relatively low-power and low-cost processors with reduced memory. The dedicated nodes are nodes that are limited in purpose and/or functionality and include nodes that are used for memory. These memory-purposed nodes are known to be designated as memory appliances.
Memory appliances are useful in several environments in the datacenter, such as acceleration of transaction processing, storing metadata for fast locking, in-memory databases for analytics and business intelligence (BI), storage caching or tier-0 storage. When used as memory expanders, memory appliances have also been shown to be effective as a remote paging device under hypervisor control. Additionally, when used to encapsulate high level abstractions (such as memcached) memory appliances are known to significantly accelerate dynamic web serving.
However, these approaches represent ad-hoc solutions that only address limited aspects at a time of memory usage in modern data centers. In other words, the conventional approaches to memory usage tend to be directed towards a single functionality and/or rely on some combination of special-purpose hardware and software. Additionally, these approaches also do not provide a uniform way of covering centralized and peer-to-peer approaches, whose combination is becoming increasingly common as the modern data center evolves and gradually introduces new functionalities.